The lithium batteries or accumulators—in their different variants such as the “lithium-ion”, “lithium-ion-polymer”, “lithium-metal-polymer” and other such batteries—are the batteries exhibiting the greatest energy density and the greatest specific energy. It is therefore the technology of choice for powering electric or hybrid vehicles, but also many portable devices. However, it is known that these batteries show a degradation of their performance levels—and notably of their capacity—over time, and do so even during the periods of non-use (then referred to as “calendar aging”). Consequently, the estimating of the state of health (or SOH) of these batteries—quantified for example by the current capacity related either to its advertised (“commercial”) value, or to its value measured when new—constitutes one of the most important tasks of the battery management systems (BMS) present in all the electric or hybrid vehicles.
This task is not easy. A number of techniques have been developed to accomplish it but not one gives full satisfaction.
Electrochemical impedance spectroscopy is a technique that is very useful for studying the aging of the batteries through the tracking of the parameters of an impedance model. However, it is complex to implement, costly and does not make it possible to access the capacity. Furthermore, it cannot be embedded in a BMS. In this respect, see:
T. Hang, D. Mukoyama, H. Nara, N. Takami, T. Momma and T. Osaka, “Electrochemical impedance spectroscopy analysis for lithium-ion battery using Li4Ti5O12 anode”, Journal of Power Sources, vol. 222, pp. 442-447, 2013; and
A. Eddahech, O. Briat, H. Henry, J.-Y. Delétage, E. Woirgard and J.-M. Vinassa, “Aging monitoring of lithium-ion cell during power cycling tests”, Microelectronics Reliability Journal, vol. 51, N° 9-11, pp. 1968-1971, 2011.
Other methods, better suited to online use, exploit the techniques of artificial intelligence, such as neural networks or fuzzy logic. See for example W. X. Shen, C. C. Chan, E. W. C. Lo and K. T. Chau, “A new battery available capacity indicator for electric vehicles using neural network”, Energy Conversion and Management, vol. 43, no. 6, pp. 817-826, 2002.
These methods implement complex algorithms, which require a significant computation power. Furthermore, they require a lengthy learning step.
Other techniques are based on identifying parameters of a model, for example by Kalman filtering. See for example:
S. Wang, M. Verbrugge, J. S. Wang and P. Liu, “Multi-parameter battery state estimator based on the adaptive and direct solution of the governing differential equations”, Journal of Power Sources, vol. 196, pp. 8735-8741, 2011; and
A. Eddahech, O. Briat and J. M. Vinassa, “Real-Time SOC and SOH Estimation for EV Li-Ion Cell Using Online Parameters Identification”, in Proc. IEEE Energy Conversion Congress and Exposition conf., 2012, Raleigh, N.C., United States.
These techniques use complex identification algorithms, requiring serious digital processing. Furthermore, the implementation thereof presupposes the availability of a fine and accurate model of the battery.
The document US 2001/0022518 teaches a method for estimating the state of health of a battery from the time t1/2 necessary for the charging current of said battery to be divided by two during the constant voltage phase of a recharge of the “constant current—constant voltage” type. This document does however show that the relationship between t1/2 and the state of health is not a one-to-one relationship.
The document FR 2 977 678 discloses a similar method, in which the state of health is estimated from the time needed for the current to cross two thresholds—defined arbitrarily—during said constant voltage charging phase.